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• VASARI Research Project - Multiple readers reviewing TCGA brain cases and evaluating them based on the VASARI feature set and evaluating the results for reader agreement along with possible connection to related clinical/genetic/pathology data collected for the TCGA.  This project is being led by Adam Flanders at Thomas Jefferson University.
• DSC T2* MR Perfusion Analysis - Survival prediction using molecular classification of glioblastomas using DSC T2* MR perfusion. This project has been accepted/presented at multiple conferences (see below) and is being led by Rajan Jain at Henry Ford Hospital.
• Prediction of outcome using clinical, imaging and genetic information - This project seeks to use the VASARI Research Project output in combination with data from the TCGA Data Portal to evaluate survival and time to recurrence.  This project is being led by Max Wintermark and Manal Nicolas Jilwan of the University of Virginia.
• Mapping of Edema/Cellular Invasion to MR Phenotypes - This project set out to present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM.  This project was led by Pascal Zinn and Rivka Colen of MDACC and BWH respectively.
• Growth Kinetics - A collaboration between Andrew Trister at Sage Bionetworks and Kristin Swanson at the University of Washington to make measurements of tumor growth kinetics in two modes (diffusion and proliferation) from pretreatment MRIs.
• Man-machine correlation of VASARI features between human and machine observers - This project is being led by Dave Gutman (Emory) and Rivka Colen (BWH).
• Analysis of Diffusion-Sensitized MRI for Predicting the Histopathologic, Genomic, and Clinical Features - This is a newer project being initiated and led by Scott Hwang at Emory University
• CAD Texture Analysis - Led by Ashlee Byrd and Brad Erickson at Mayo, they are using multispectral features, including intensity, texture, and morphology, to identify imaging features that predict genetic patterns.
• Clustering (supervised & unsupervised) of GBM data - Cases are clustered into semantically-distinct categories using image-derived features, followed by examination of genomic correlates from the obtained clusters.  Led by Arvind Rao, Jim Chen, and Adam Flanders.
• Stanford TCGA radiogenomics project:  We are studying computational image features that characterize shape, texture and size of glioblastoma multiforme patients. More specifically, we extract computational image features from MRI images and investigate their clinical relevance and correlation with molecular data.  Investigators: Olivier Gevaert, Sylvia Plevritis.
• Quantitative imaging features extraction from perfusion imaging - We are extracting perfusion features from different anatomical regions of GBM tumors (enhancing and necrotic regions) to quantify tumor heterogeneity. We are working on correlating perfusion imaging features with molecular characterization as well as defining subtypes of GBM predictive of overall survival using molecular and perfusion imaging features. The investigators of this project are Tiffany Liu and Daniel Rubin at Stanford University.

Group Publications

Note: For more information on how Shared Lists are used to cite and share data please view our TCIA Citation Guidelines.

References

This section contains papers, presentations, and videos from the genomics/clinical/pathology perspectives which may be of interest to the glioma imaging groupThe following links contain additional publications from the main TCGA project, as well as their posted publication guidelines.

• TCGA Publications
• TCGA Publication Guidelines 

2nd Annual TCGA Symposium

• The Somatic Genomic Landscape of Glioblastoma Multiforme
  • Roel Verhaak, PhD, The University of Texas MD Anderson Cancer Center
  • Video | Slides

Selected Publications

• Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, Miller CR, Ding L, Golub T, Mesirov JP, Alexe G, Lawrence M, O'Kelly M, Tamayo P, Weir BA, Gabriel S, Winckler W, Gupta S, Jakkula L, Feiler HS, Hodgson JG, James CD, Sarkaria JN, Brennan C, Kahn A, Spellman PT, Wilson RK, Speed TP, Gray JW, Meyerson M, Getz G, Perou CM, Hayes DN; Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010 Jan 19;17(1):98-110. doi: 10.1016/j.ccr.2009.12.020. (Full Text, verhaak-patient-characteristics.xls)
• Cooper LAD, Gutman DA, Chisolm C, Appin C, Kong J, Rong Y, Kurc T, Van Meir EG, Saltz JH, Moreno CS, Brat DJ. The Tumor Microenvironment Strongly Impacts Master Transcriptional Regulators and Gene Expression Class of Glioblastoma. American Journal of Pathology 180(5):2108-19, May 2012 (Link)
• Cooper LAD, Kong J, Gutman DA, Wang F, Gao J, Appin C, Cholleti S, Pan T, Sharma A, Scarpace L, Mikkelsen T, Kurc T, Moreno CS, Brat DJ, Saltz JH. Integrated Morphologic Analysis for the Identification and Characterization of Disease Subtypes. J Am Med Inform Assoc. 19(2):317-23, Mar-Apr 2012 (Full Text)

Conference Abstracts

RSNA 2012 (Nov 25-Nov 30, 2012, Chicago, IL)

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